This article is the second in a series focusing on the major themes that emerged from the recent ILTACON conference. Read Part 1.
In the years leading up to the pandemic, many discussions on Artificial Intelligence (AI) at legal conferences tended to be abstract and either too elementary or too advanced. Many devolved into arguments about what AI is and the endless discussions of whether AI is a job-stealer (spoiler alert: it’s not).
The intervening years have resulted in a more matter-of-fact approach to using AI in legal practice. Machine learning and natural language processing are embedded in many tools that lawyers use to do their jobs and run their businesses today. This time, the AI-related sessions were more practical, “here’s how we are doing it” discussions rather than AI evangelism.
- Jeff Marple from Keesal Propulsion Labs and Alan Velasco of Mayer Brown walked through some case studies of how they have used machine learning tools to analyze contracts and extract data to solve specific client needs.
- Another session (co-led by Litera’s Ben Kim and Andrew Ward of Time by Ping) demonstrated the process of evaluating the results of machine learning models to assess their success.
- Even one of the more theoretical sessions, featuring a discussion on the difference between rules-based AI systems and natural language processing, was grounded in the practical. Pablo Arredondo of Casetext, representing the NLP approach, and Damien Riehl of Fastcase and SALI provided examples of the trade-offs and compromises in using different systems for different applications.
AI is finally finding a home in legal tech. The pragmatists are winning; legal organizations are recognizing the use cases in which AI adds value to the work of legal professionals and eagerly adopting products in which AI is embedded.
Stay tuned for the next blog post: Part 3 - Patience, Incrementalism, and “Use What You Have”.
Posted in ILTACON